Social Integration and its association with mortality among older people in China

Ph.DDOCTOR OF PHILOSOPH

Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors

While deep neural networks have achieved great success in graph analysis, recent work has shown that they are vulnerable to adversarial attacks. Compared with adversarial attacks on image classification, performing adversarial attacks on graphs is more challenging because of the discrete and non-differential nature of the adjacent matrix for a graph. In this work, we propose Cluster Attack -- a Graph Injection Attack (GIA) on node classification, which injects fake nodes into the original graph to degenerate the performance of graph neural networks (GNNs) on certain victim nodes while affecting the other nodes as little as possible. We demonstrate that a GIA problem can be equivalently formulated as a graph clustering problem; thus, the discrete optimization problem of the adjacency matrix can be solved in the context of graph clustering. In particular, we propose to measure the similarity between victim nodes by a metric of Adversarial Vulnerability, which is related to how the victim nodes will be affected by the injected fake node, and to cluster the victim nodes accordingly. Our attack is performed in a practical and unnoticeable query-based black-box manner with only a few nodes on the graphs that can be accessed. Theoretical analysis and extensive experiments demonstrate the effectiveness of our method by fooling the node classifiers with only a small number of queries.Comment: IJCAI 2022 (Long Presentation

A Sophisticated Method of the Mechanical Design of Cable Accessories Focusing on Interface Contact Pressure

The most critical positions of a prefabricated cable accessory, from the electrical point of view, are the interfaces between the stress cone and its surroundings. Accordingly, the contact pressure on those interfaces needs to be carefully designed to assure both good dielectric strength and smooth installation of the stress cone. Nevertheless, since stress cones made from rubber are under large deformation after installation, their internal stress distribution is neither practical to measure directly by planting sensors, nor feasible to compute accurately with the conventional theory of linear structural mechanics. This paper presents one sophisticated method for computing the mechanical stress distribution in rubber stress cones of cable accessories by employing hyperelastic models in a computation model based on the finite element method. This method offers accurate results for rubber bodies of complex geometries and large deformations. Based on the method, a case study of a composite prefabricated termination for extruded cables is presented, and the sensitivity analysis is given as well

Experimental study on adhesion of oxide scale on hot-rolled steel strip

An experimental method was developed to study the adherence properties of the oxide scale formed on microalloyed low carbon steel after hot strip rolling. The evolution of the oxide scale during laminar cooling was investigated using Gleeble 3500 Thermal-Mechanical Simulator connected with a humid air generator. After the sample cooled down to ambient temperature, the oxide scale was protected by lacquer to prevent the scale from losing. Physicochemical characteristics of the oxide scale were examined and the adherence mechanism was discussed. Decomposed wustite a mixture of α-iron and magnetite (Fe3O4), can substantially improve the integrity of oxide scale. However, large quantities of hematite (Fe2O3) or retained wustite (FeO) were found detrimental to the adhesion of the oxide scale. It is found that the adherence of oxide scales significantly depends on the phase composition of oxide scales with different thickness

Polyvinyl Alcohol/Graphene Oxide Conductive Hydrogels via the Synergy of Freezing and Salting Out for Strain Sensors

Hydrogels of flexibility, strength, and conductivity have demonstrated broad applications in wearable electronics and soft robotics. However, it is still a challenge to fabricate conductive hydrogels with high strength massively and economically. Herein, a simple strategy is proposed to design a strong ionically conductive hydrogel. This ion-conducting hydrogel was obtained under the synergistic action by salting out the frozen mixture of polyvinyl alcohol (PVA) and graphene oxide (GO) using a high concentration of sodium chloride solution. The developed hydrogel containing only 5 wt% PVA manifests good tensile stress (65 kPa) and elongation (180%). Meanwhile, the PVA matrix doped with a small amount of GO formed uniformly porous ion channels after salting out, endowed the PVA/GO hydrogel with excellent ionic conductivity (up to 3.38 S m; -1; ). Therefore, the fabricated PVA/GO hydrogel, anticipated for a strain sensor, exhibits good sensitivity (Gauge factor = 2.05 at 100% strain), satisfying working stability (stably cycled for 10 min), and excellent recognition ability. This facile method to prepare conductive hydrogels displays translational potential in flexible electronics for engineering applications

Labor Law and Innovation Revisited

This paper examines the impact of changes in job security on corporate innovation in 20 non-U.S. OECD countries. Using a difference-in-differences approach, we provide firm-level evidence that the enhancement of labor protection has a negative impact on innovation. We then discuss possible channels and find that employee-friendly labor reforms induce inventor shirking and a distortion in labor flow. Further investigation reveals that the negative relation is more pronounced in (1) firms that heavily rely on external financing, (2) firms that have high R&D intensity, (3) manufacturing industries, and (4) civil-law countries. Our micro-level evidence indicates that enhanced employment protection impedes corporate innovation

Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion

Sequential recommendation aims to recommend the next item that matches a user's interest, based on the sequence of items he/she interacted with before. Scrutinizing previous studies, we can summarize a common learning-to-classify paradigm -- given a positive item, a recommender model performs negative sampling to add negative items and learns to classify whether the user prefers them or not, based on his/her historical interaction sequence. Although effective, we reveal two inherent limitations:(1) it may differ from human behavior in that a user could imagine an oracle item in mind and select potential items matching the oracle; and (2) the classification is limited in the candidate pool with noisy or easy supervision from negative samples, which dilutes the preference signals towards the oracle item. Yet, generating the oracle item from the historical interaction sequence is mostly unexplored. To bridge the gap, we reshape sequential recommendation as a learning-to-generate paradigm, which is achieved via a guided diffusion model, termed DreamRec.Specifically, for a sequence of historical items, it applies a Transformer encoder to create guidance representations. Noising target items explores the underlying distribution of item space; then, with the guidance of historical interactions, the denoising process generates an oracle item to recover the positive item, so as to cast off negative sampling and depict the true preference of the user directly. We evaluate the effectiveness of DreamRec through extensive experiments and comparisons with existing methods. Codes and data are open-sourced at https://github.com/YangZhengyi98/DreamRec

Mechanics of design and model development of CVC-plus roll curve

The mathematic model of CVC-Plus work roll curve is built. The ratio of the initial shifting value to the target crown is determined, and the mathematical model considering the relationship between the coefficients A2, A3, A4, A5 and is established. According to the theoretical analysis, the distance between the maximum or minimum point of the high order equivalent crown for work roll with CVC-plus roll curve and the rolling central point is the times of the roll barrel length. In general, the initial shifting value of the CVC-plus roll curve is not equal to the initial shifting value of the 3-order CVC roll curve . The coefficient A1 can also be obtained by optimizing the target function with minimizing the axial force

Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes

PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools monitoring and prioritizing the literature to understand the clinical implications of the pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations. METHODS: We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated dataset for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule based on the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule based on the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS: For penetrance classification, we annotated 3740 paper titles and abstracts and used 60% for training the model, 20% for tuning the model, and 20% for evaluating the model. The SVM model achieves 89.53% accuracy (percentage of papers that were correctly classified) while the CNN model achieves 88.95 % accuracy. For prevalence classification, we annotated 3753 paper titles and abstracts. The SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 % accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene-cancer associations and keep the knowledge bases for clinical decision support tools up to date